Overview

Dataset statistics

Number of variables40
Number of observations3533211
Missing cells69829472
Missing cells (%)49.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 GiB
Average record size in memory320.0 B

Variable types

Categorical16
Numeric18
Unsupported6

Alerts

id_mutation has a high cardinality: 1514872 distinct values High cardinality
date_mutation has a high cardinality: 365 distinct values High cardinality
adresse_nom_voie has a high cardinality: 506794 distinct values High cardinality
adresse_code_voie has a high cardinality: 16746 distinct values High cardinality
nom_commune has a high cardinality: 30590 distinct values High cardinality
ancien_nom_commune has a high cardinality: 192 distinct values High cardinality
id_parcelle has a high cardinality: 2142850 distinct values High cardinality
ancien_id_parcelle has a high cardinality: 3366 distinct values High cardinality
code_nature_culture_speciale has a high cardinality: 120 distinct values High cardinality
nature_culture_speciale has a high cardinality: 120 distinct values High cardinality
code_postal is highly correlated with ancien_code_communeHigh correlation
ancien_code_commune is highly correlated with code_postal and 1 other fieldsHigh correlation
lot1_surface_carrez is highly correlated with surface_reelle_bati and 2 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot3_surface_carrez and 4 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot2_surface_carrez and 3 other fieldsHigh correlation
lot4_numero is highly correlated with ancien_code_commune and 1 other fieldsHigh correlation
lot4_surface_carrez is highly correlated with lot2_surface_carrez and 2 other fieldsHigh correlation
lot5_numero is highly correlated with lot4_numeroHigh correlation
lot5_surface_carrez is highly correlated with lot2_surface_carrez and 2 other fieldsHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
surface_reelle_bati is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
nombre_pieces_principales is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
surface_terrain is highly correlated with lot1_surface_carrezHigh correlation
code_postal is highly correlated with ancien_code_communeHigh correlation
ancien_code_commune is highly correlated with code_postal and 1 other fieldsHigh correlation
lot1_surface_carrez is highly correlated with lot5_surface_carrezHigh correlation
lot2_surface_carrez is highly correlated with lot3_surface_carrez and 1 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot2_surface_carrez and 2 other fieldsHigh correlation
lot4_numero is highly correlated with ancien_code_commune and 1 other fieldsHigh correlation
lot4_surface_carrez is highly correlated with lot3_surface_carrez and 1 other fieldsHigh correlation
lot5_numero is highly correlated with lot4_numeroHigh correlation
lot5_surface_carrez is highly correlated with lot1_surface_carrez and 2 other fieldsHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
surface_reelle_bati is highly correlated with lot4_surface_carrezHigh correlation
nombre_pieces_principales is highly correlated with code_type_localHigh correlation
code_postal is highly correlated with ancien_code_communeHigh correlation
ancien_code_commune is highly correlated with code_postal and 1 other fieldsHigh correlation
lot1_surface_carrez is highly correlated with surface_reelle_bati and 1 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot5_surface_carrez and 2 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot4_surface_carrez and 2 other fieldsHigh correlation
lot4_numero is highly correlated with ancien_code_commune and 1 other fieldsHigh correlation
lot4_surface_carrez is highly correlated with lot3_surface_carrez and 1 other fieldsHigh correlation
lot5_numero is highly correlated with lot4_numeroHigh correlation
lot5_surface_carrez is highly correlated with lot2_surface_carrez and 2 other fieldsHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
surface_reelle_bati is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
nombre_pieces_principales is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
code_type_local is highly correlated with type_localHigh correlation
code_nature_culture is highly correlated with nature_cultureHigh correlation
type_local is highly correlated with code_type_localHigh correlation
nature_culture is highly correlated with code_nature_cultureHigh correlation
adresse_numero is highly correlated with adresse_suffixeHigh correlation
adresse_suffixe is highly correlated with adresse_numero and 1 other fieldsHigh correlation
code_postal is highly correlated with ancien_code_commune and 1 other fieldsHigh correlation
ancien_code_commune is highly correlated with adresse_suffixe and 1 other fieldsHigh correlation
lot1_surface_carrez is highly correlated with lot2_surface_carrez and 3 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
lot4_numero is highly correlated with lot5_numeroHigh correlation
lot4_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
lot5_numero is highly correlated with lot4_numeroHigh correlation
lot5_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
code_type_local is highly correlated with type_localHigh correlation
type_local is highly correlated with code_type_localHigh correlation
code_nature_culture is highly correlated with nature_cultureHigh correlation
nature_culture is highly correlated with code_nature_cultureHigh correlation
longitude is highly correlated with code_postal and 1 other fieldsHigh correlation
latitude is highly correlated with longitudeHigh correlation
valeur_fonciere has 40825 (1.2%) missing values Missing
adresse_numero has 1447761 (41.0%) missing values Missing
adresse_suffixe has 3380766 (95.7%) missing values Missing
ancien_code_commune has 3528985 (99.9%) missing values Missing
ancien_nom_commune has 3528985 (99.9%) missing values Missing
ancien_id_parcelle has 3529056 (99.9%) missing values Missing
numero_volume has 3523727 (99.7%) missing values Missing
lot1_numero has 2436238 (69.0%) missing values Missing
lot1_surface_carrez has 3234737 (91.6%) missing values Missing
lot2_numero has 3304923 (93.5%) missing values Missing
lot2_surface_carrez has 3458801 (97.9%) missing values Missing
lot3_numero has 3495391 (98.9%) missing values Missing
lot3_surface_carrez has 3525820 (99.8%) missing values Missing
lot4_numero has 3520085 (99.6%) missing values Missing
lot4_surface_carrez has 3531180 (99.9%) missing values Missing
lot5_numero has 3526863 (99.8%) missing values Missing
lot5_surface_carrez has 3532265 (> 99.9%) missing values Missing
code_type_local has 1632190 (46.2%) missing values Missing
type_local has 1632190 (46.2%) missing values Missing
surface_reelle_bati has 2090899 (59.2%) missing values Missing
nombre_pieces_principales has 1634823 (46.3%) missing values Missing
code_nature_culture has 1112059 (31.5%) missing values Missing
nature_culture has 1112059 (31.5%) missing values Missing
code_nature_culture_speciale has 3375916 (95.5%) missing values Missing
nature_culture_speciale has 3375916 (95.5%) missing values Missing
surface_terrain has 1112128 (31.5%) missing values Missing
longitude has 51669 (1.5%) missing values Missing
latitude has 51669 (1.5%) missing values Missing
numero_disposition is highly skewed (γ1 = 81.2603909) Skewed
valeur_fonciere is highly skewed (γ1 = 42.3720987) Skewed
lot1_surface_carrez is highly skewed (γ1 = 44.33723987) Skewed
lot2_surface_carrez is highly skewed (γ1 = 67.65985445) Skewed
lot4_numero is highly skewed (γ1 = 75.84754127) Skewed
lot5_numero is highly skewed (γ1 = 54.84380155) Skewed
nombre_lots is highly skewed (γ1 = 39.52830246) Skewed
surface_reelle_bati is highly skewed (γ1 = 162.9347308) Skewed
surface_terrain is highly skewed (γ1 = 45.59182149) Skewed
ancien_id_parcelle is uniformly distributed Uniform
code_commune is an unsupported type, check if it needs cleaning or further analysis Unsupported
code_departement is an unsupported type, check if it needs cleaning or further analysis Unsupported
numero_volume is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot1_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot2_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot3_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
nombre_lots has 2436238 (69.0%) zeros Zeros
nombre_pieces_principales has 587272 (16.6%) zeros Zeros

Reproduction

Analysis started2021-10-05 22:08:48.357661
Analysis finished2021-10-05 22:25:43.240396
Duration16 minutes and 54.88 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id_mutation
Categorical

HIGH CARDINALITY

Distinct1514872
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size27.0 MiB
2019-171544
 
2778
2019-268092
 
2736
2019-384592
 
2164
2019-178592
 
2016
2019-1437512
 
1700
Other values (1514867)
3521817 

Length

Max length12
Median length11
Mean length11.24444422
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique726052 ?
Unique (%)20.5%

Sample

1st row2019-1
2nd row2019-2
3rd row2019-2
4th row2019-3
5th row2019-4

Common Values

ValueCountFrequency (%)
2019-1715442778
 
0.1%
2019-2680922736
 
0.1%
2019-3845922164
 
0.1%
2019-1785922016
 
0.1%
2019-14375121700
 
< 0.1%
2019-11018711658
 
< 0.1%
2019-13252421543
 
< 0.1%
2019-10579351409
 
< 0.1%
2019-2309521395
 
< 0.1%
2019-10441111271
 
< 0.1%
Other values (1514862)3514541
99.5%

Length

2021-10-06T00:25:43.487845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-1715442778
 
0.1%
2019-2680922736
 
0.1%
2019-3845922164
 
0.1%
2019-1785922016
 
0.1%
2019-14375121700
 
< 0.1%
2019-11018711658
 
< 0.1%
2019-13252421543
 
< 0.1%
2019-10579351409
 
< 0.1%
2019-2309521395
 
< 0.1%
2019-10441111271
 
< 0.1%
Other values (1514862)3514541
99.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

date_mutation
Categorical

HIGH CARDINALITY

Distinct365
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.0 MiB
2019-12-20
 
36454
2019-06-28
 
29693
2019-12-27
 
27606
2019-12-19
 
27075
2019-12-23
 
25723
Other values (360)
3386660 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2019-01-04
2nd row2019-01-04
3rd row2019-01-04
4th row2019-01-08
5th row2019-01-07

Common Values

ValueCountFrequency (%)
2019-12-2036454
 
1.0%
2019-06-2829693
 
0.8%
2019-12-2727606
 
0.8%
2019-12-1927075
 
0.8%
2019-12-2325723
 
0.7%
2019-03-2925501
 
0.7%
2019-11-2924992
 
0.7%
2019-12-3024653
 
0.7%
2019-12-1823998
 
0.7%
2019-05-2923152
 
0.7%
Other values (355)3264364
92.4%

Length

2021-10-06T00:25:43.740865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-12-2036454
 
1.0%
2019-06-2829693
 
0.8%
2019-12-2727606
 
0.8%
2019-12-1927075
 
0.8%
2019-12-2325723
 
0.7%
2019-03-2925501
 
0.7%
2019-11-2924992
 
0.7%
2019-12-3024653
 
0.7%
2019-12-1823998
 
0.7%
2019-05-2923152
 
0.7%
Other values (355)3264364
92.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

numero_disposition
Real number (ℝ≥0)

SKEWED

Distinct627
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.18635966
Minimum1
Maximum694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:44.031763image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum694
Range693
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.333766329
Coefficient of variation (CV)5.338824763
Kurtosis7182.774453
Mean1.18635966
Median Absolute Deviation (MAD)0
Skewness81.2603909
Sum4191659
Variance40.11659592
MonotonicityNot monotonic
2021-10-06T00:25:44.350344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13315151
93.8%
2179269
 
5.1%
324353
 
0.7%
44631
 
0.1%
51743
 
< 0.1%
61250
 
< 0.1%
7855
 
< 0.1%
10469
 
< 0.1%
11437
 
< 0.1%
9364
 
< 0.1%
Other values (617)4689
 
0.1%
ValueCountFrequency (%)
13315151
93.8%
2179269
 
5.1%
324353
 
0.7%
44631
 
0.1%
51743
 
< 0.1%
61250
 
< 0.1%
7855
 
< 0.1%
8356
 
< 0.1%
9364
 
< 0.1%
10469
 
< 0.1%
ValueCountFrequency (%)
6941
< 0.1%
6931
< 0.1%
6921
< 0.1%
6911
< 0.1%
6901
< 0.1%
6891
< 0.1%
6881
< 0.1%
6871
< 0.1%
6861
< 0.1%
6851
< 0.1%

nature_mutation
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.0 MiB
Vente
3205120 
Vente en l'état futur d'achèvement
 
261337
Echange
 
41612
Vente terrain à bâtir
 
10758
Adjudication
 
10435

Length

Max length34
Median length5
Mean length7.246897227
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVente
2nd rowVente
3rd rowVente
4th rowVente
5th rowVente

Common Values

ValueCountFrequency (%)
Vente3205120
90.7%
Vente en l'état futur d'achèvement261337
 
7.4%
Echange41612
 
1.2%
Vente terrain à bâtir10758
 
0.3%
Adjudication10435
 
0.3%
Expropriation3949
 
0.1%

Length

2021-10-06T00:25:44.686961image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-06T00:25:44.902009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
vente3477215
75.4%
d'achèvement261337
 
5.7%
futur261337
 
5.7%
l'état261337
 
5.7%
en261337
 
5.7%
echange41612
 
0.9%
bâtir10758
 
0.2%
à10758
 
0.2%
terrain10758
 
0.2%
adjudication10435
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

valeur_fonciere
Real number (ℝ≥0)

MISSING
SKEWED

Distinct138730
Distinct (%)4.0%
Missing40825
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1027460.827
Minimum0.01
Maximum2086000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:45.234591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2600
Q160000
median149000
Q3270000
95-th percentile1500000
Maximum2086000000
Range2086000000
Interquartile range (IQR)210000

Descriptive statistics

Standard deviation8156290.693
Coefficient of variation (CV)7.938298456
Kurtosis5534.136597
Mean1027460.827
Median Absolute Deviation (MAD)100000
Skewness42.3720987
Sum3.588289806 × 1012
Variance6.652507787 × 1013
MonotonicityNot monotonic
2021-10-06T00:25:45.564616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000030089
 
0.9%
15000029164
 
0.8%
12000027417
 
0.8%
127396
 
0.8%
8000025365
 
0.7%
20000024130
 
0.7%
13000024114
 
0.7%
11000023723
 
0.7%
5000023545
 
0.7%
14000023154
 
0.7%
Other values (138720)3234289
91.5%
(Missing)40825
 
1.2%
ValueCountFrequency (%)
0.013
 
< 0.1%
0.112
 
< 0.1%
0.15398
< 0.1%
0.163
 
< 0.1%
0.1836
 
< 0.1%
0.192
 
< 0.1%
0.34
 
< 0.1%
0.338
 
< 0.1%
0.343
 
< 0.1%
0.520
 
< 0.1%
ValueCountFrequency (%)
20860000002
 
< 0.1%
17500000003
 
< 0.1%
8450000002
 
< 0.1%
69018675024
< 0.1%
68566797031
< 0.1%
6129904606
 
< 0.1%
5946096003
 
< 0.1%
5274000005
 
< 0.1%
4000000001
 
< 0.1%
3850000001
 
< 0.1%

adresse_numero
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7299
Distinct (%)0.3%
Missing1447761
Missing (%)41.0%
Infinite0
Infinite (%)0.0%
Mean770.1700909
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:45.871686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median26
Q3100
95-th percentile5727
Maximum9999
Range9998
Interquartile range (IQR)92

Descriptive statistics

Standard deviation2115.060468
Coefficient of variation (CV)2.746225143
Kurtosis7.513289879
Mean770.1700909
Median Absolute Deviation (MAD)22
Skewness2.943936252
Sum1606151216
Variance4473480.782
MonotonicityNot monotonic
2021-10-06T00:25:46.176754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
193526
 
2.6%
281794
 
2.3%
365678
 
1.9%
464887
 
1.8%
558320
 
1.7%
657097
 
1.6%
750952
 
1.4%
850044
 
1.4%
946163
 
1.3%
1045629
 
1.3%
Other values (7289)1471360
41.6%
(Missing)1447761
41.0%
ValueCountFrequency (%)
193526
2.6%
281794
2.3%
365678
1.9%
464887
1.8%
558320
1.7%
657097
1.6%
750952
1.4%
850044
1.4%
946163
1.3%
1045629
1.3%
ValueCountFrequency (%)
9999491
< 0.1%
999893
 
< 0.1%
999717
 
< 0.1%
999621
 
< 0.1%
999521
 
< 0.1%
999414
 
< 0.1%
99933
 
< 0.1%
99927
 
< 0.1%
999120
 
< 0.1%
999010
 
< 0.1%

adresse_suffixe
Categorical

HIGH CORRELATION
MISSING

Distinct40
Distinct (%)< 0.1%
Missing3380766
Missing (%)95.7%
Memory size27.0 MiB
B
88283 
A
24458 
F
13635 
T
11587 
C
 
5152
Other values (35)
9330 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowB
2nd rowZ
3rd rowB
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
B88283
 
2.5%
A24458
 
0.7%
F13635
 
0.4%
T11587
 
0.3%
C5152
 
0.1%
D2439
 
0.1%
E1395
 
< 0.1%
Q1275
 
< 0.1%
P724
 
< 0.1%
G609
 
< 0.1%
Other values (30)2888
 
0.1%
(Missing)3380766
95.7%

Length

2021-10-06T00:25:46.486146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b88283
57.9%
a24458
 
16.0%
f13635
 
8.9%
t11587
 
7.6%
c5152
 
3.4%
d2439
 
1.6%
e1395
 
0.9%
q1275
 
0.8%
p724
 
0.5%
g609
 
0.4%
Other values (27)2888
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

adresse_nom_voie
Categorical

HIGH CARDINALITY

Distinct506794
Distinct (%)14.5%
Missing33838
Missing (%)1.0%
Memory size27.0 MiB
LE VILLAGE
 
32178
LE BOURG
 
28220
AV JEAN JAURES
 
7143
GR GRANDE RUE
 
7114
RUE DE LA REPUBLIQUE
 
6371
Other values (506789)
3418347 

Length

Max length31
Median length14
Mean length14.72676219
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172968 ?
Unique (%)4.9%

Sample

1st rowRUE DE MONTHOLON
2nd rowRUE GEN DELESTRAINT
3rd rowRUE GEN DELESTRAINT
4th rowRUE DES CHAMPAGNES
5th rowLOT LE BIOLAY

Common Values

ValueCountFrequency (%)
LE VILLAGE32178
 
0.9%
LE BOURG28220
 
0.8%
AV JEAN JAURES7143
 
0.2%
GR GRANDE RUE7114
 
0.2%
RUE DE LA REPUBLIQUE6371
 
0.2%
RUE JEAN JAURES6156
 
0.2%
RUE PASTEUR5574
 
0.2%
RUE VICTOR HUGO5201
 
0.1%
AV DE LA REPUBLIQUE5129
 
0.1%
RUE DE PARIS4361
 
0.1%
Other values (506784)3391926
96.0%
(Missing)33838
 
1.0%

Length

2021-10-06T00:25:46.848234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rue1177070
 
11.7%
de768672
 
7.6%
la519788
 
5.2%
du353758
 
3.5%
le306217
 
3.0%
des301752
 
3.0%
av270241
 
2.7%
les240278
 
2.4%
che113774
 
1.1%
rte111467
 
1.1%
Other values (209127)5908361
58.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

adresse_code_voie
Categorical

HIGH CARDINALITY

Distinct16746
Distinct (%)0.5%
Missing33771
Missing (%)1.0%
Memory size27.0 MiB
B005
 
15947
B007
 
15302
B003
 
15299
B014
 
15037
B017
 
14877
Other values (16741)
3422978 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1504 ?
Unique (%)< 0.1%

Sample

1st row2730
2nd row1650
3rd row1650
4th row0043
5th rowA003

Common Values

ValueCountFrequency (%)
B00515947
 
0.5%
B00715302
 
0.4%
B00315299
 
0.4%
B01415037
 
0.4%
B01714877
 
0.4%
B00614826
 
0.4%
B00214820
 
0.4%
B01014740
 
0.4%
B00114734
 
0.4%
002014702
 
0.4%
Other values (16736)3349156
94.8%
(Missing)33771
 
1.0%

Length

2021-10-06T00:25:47.141299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b00515947
 
0.5%
b00715302
 
0.4%
b00315299
 
0.4%
b01415037
 
0.4%
b01714877
 
0.4%
b00614826
 
0.4%
b00214820
 
0.4%
b01014740
 
0.4%
b00114734
 
0.4%
002014702
 
0.4%
Other values (16736)3349156
95.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_postal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5862
Distinct (%)0.2%
Missing33937
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean50443.2464
Minimum1000
Maximum97490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:47.451370image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile6540
Q129200
median49280
Q375016
95-th percentile93130
Maximum97490
Range96490
Interquartile range (IQR)45816

Descriptive statistics

Standard deviation27505.41514
Coefficient of variation (CV)0.5452744838
Kurtosis-1.200231695
Mean50443.2464
Median Absolute Deviation (MAD)24060
Skewness-0.006414740661
Sum1.765147406 × 1011
Variance756547862.1
MonotonicityNot monotonic
2021-10-06T00:25:47.754437image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210008593
 
0.2%
691008256
 
0.2%
750157867
 
0.2%
180007830
 
0.2%
540007612
 
0.2%
312007175
 
0.2%
750167019
 
0.2%
511006925
 
0.2%
350006733
 
0.2%
292006570
 
0.2%
Other values (5852)3424694
96.9%
(Missing)33937
 
1.0%
ValueCountFrequency (%)
10001892
0.1%
1090462
 
< 0.1%
11001153
< 0.1%
1110807
 
< 0.1%
1120920
 
< 0.1%
1130453
 
< 0.1%
1140646
 
< 0.1%
11501158
< 0.1%
1160896
 
< 0.1%
11702302
0.1%
ValueCountFrequency (%)
974901855
0.1%
97480613
 
< 0.1%
97470388
 
< 0.1%
97460732
 
< 0.1%
97450271
 
< 0.1%
9744247
 
< 0.1%
97441370
 
< 0.1%
97440879
< 0.1%
97439114
 
< 0.1%
97438644
 
< 0.1%

code_commune
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size27.0 MiB

nom_commune
Categorical

HIGH CARDINALITY

Distinct30590
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size27.0 MiB
Toulouse
 
29142
Nice
 
18541
Nantes
 
16670
Montpellier
 
14816
Bordeaux
 
13574
Other values (30585)
3440468 

Length

Max length45
Median length10
Mean length11.89568299
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique334 ?
Unique (%)< 0.1%

Sample

1st rowBourg-en-Bresse
2nd rowBourg-en-Bresse
3rd rowBourg-en-Bresse
4th rowPriay
5th rowSaint-Étienne-du-Bois

Common Values

ValueCountFrequency (%)
Toulouse29142
 
0.8%
Nice18541
 
0.5%
Nantes16670
 
0.5%
Montpellier14816
 
0.4%
Bordeaux13574
 
0.4%
Lille12182
 
0.3%
Rennes11384
 
0.3%
Nîmes8921
 
0.3%
Le Havre8842
 
0.3%
Dijon8668
 
0.2%
Other values (30580)3390471
96.0%

Length

2021-10-06T00:25:48.273556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arrondissement124276
 
3.0%
la108063
 
2.6%
le102566
 
2.5%
paris68183
 
1.7%
les38342
 
0.9%
marseille34357
 
0.8%
toulouse29142
 
0.7%
lyon21736
 
0.5%
nice18541
 
0.5%
nantes16670
 
0.4%
Other values (30496)3539720
86.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_departement
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size27.0 MiB

ancien_code_commune
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct192
Distinct (%)4.5%
Missing3528985
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean52133.70303
Minimum1059
Maximum91182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:48.565643image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1059
5-th percentile1414
Q135011
median50516
Q378884
95-th percentile89448
Maximum91182
Range90123
Interquartile range (IQR)43873

Descriptive statistics

Standard deviation26055.09046
Coefficient of variation (CV)0.4997744058
Kurtosis-0.8447448861
Mean52133.70303
Median Absolute Deviation (MAD)22404
Skewness-0.2432193738
Sum220317029
Variance678867738.6
MonotonicityNot monotonic
2021-10-06T00:25:48.831703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91182188
 
< 0.1%
49065186
 
< 0.1%
53014152
 
< 0.1%
89448136
 
< 0.1%
41257111
 
< 0.1%
78251109
 
< 0.1%
7852476
 
< 0.1%
5058172
 
< 0.1%
1466671
 
< 0.1%
7917167
 
< 0.1%
Other values (182)3058
 
0.1%
(Missing)3528985
99.9%
ValueCountFrequency (%)
10593
 
< 0.1%
109163
< 0.1%
109726
< 0.1%
112239
< 0.1%
11864
 
< 0.1%
120528
< 0.1%
121813
 
< 0.1%
12215
 
< 0.1%
134111
 
< 0.1%
14134
 
< 0.1%
ValueCountFrequency (%)
91182188
< 0.1%
900731
 
< 0.1%
89448136
< 0.1%
8938114
 
< 0.1%
8934011
 
< 0.1%
891092
 
< 0.1%
8718417
 
< 0.1%
8717345
 
< 0.1%
8627861
 
< 0.1%
8618824
 
< 0.1%

ancien_nom_commune
Categorical

HIGH CARDINALITY
MISSING

Distinct192
Distinct (%)4.5%
Missing3528985
Missing (%)99.9%
Memory size27.0 MiB
Courcouronnes
 
188
Champigné
 
186
Azé
 
152
Vignes
 
136
Thenay
 
111
Other values (187)
3453 

Length

Max length26
Median length10
Mean length11.98414576
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.3%

Sample

1st rowLancrans
2nd rowLancrans
3rd rowChâtillon-en-Michaille
4th rowChâtillon-en-Michaille
5th rowLancrans

Common Values

ValueCountFrequency (%)
Courcouronnes188
 
< 0.1%
Champigné186
 
< 0.1%
Azé152
 
< 0.1%
Vignes136
 
< 0.1%
Thenay111
 
< 0.1%
Fourqueux109
 
< 0.1%
Rocquencourt76
 
< 0.1%
Soulles72
 
< 0.1%
Sannerville71
 
< 0.1%
Mauzé-Thouarsais67
 
< 0.1%
Other values (182)3058
 
0.1%
(Missing)3528985
99.9%

Length

2021-10-06T00:25:49.137772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
courcouronnes188
 
4.2%
champigné186
 
4.1%
le173
 
3.8%
azé152
 
3.4%
vignes136
 
3.0%
thenay111
 
2.5%
fourqueux109
 
2.4%
la78
 
1.7%
rocquencourt76
 
1.7%
soulles72
 
1.6%
Other values (185)3232
71.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

id_parcelle
Categorical

HIGH CARDINALITY

Distinct2142850
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Memory size27.0 MiB
95280000AC0239
 
1693
91345000AO0357
 
1044
783220000F0122
 
1013
910270000E0576
 
971
930530000J0092
 
806
Other values (2142845)
3527684 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1709064 ?
Unique (%)48.4%

Sample

1st row01053000AI0298
2nd row01053000AM0095
3rd row01053000AM0095
4th row013140000E1676
5th row01350000AA0011

Common Values

ValueCountFrequency (%)
95280000AC02391693
 
< 0.1%
91345000AO03571044
 
< 0.1%
783220000F01221013
 
< 0.1%
910270000E0576971
 
< 0.1%
930530000J0092806
 
< 0.1%
78242000AC0403697
 
< 0.1%
93031000AU0124687
 
< 0.1%
13001000IX0113630
 
< 0.1%
75114000DM0007595
 
< 0.1%
30189000EM0022502
 
< 0.1%
Other values (2142840)3524573
99.8%

Length

2021-10-06T00:25:49.559487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
95280000ac02391693
 
< 0.1%
91345000ao03571044
 
< 0.1%
783220000f01221013
 
< 0.1%
910270000e0576971
 
< 0.1%
930530000j0092806
 
< 0.1%
78242000ac0403697
 
< 0.1%
93031000au0124687
 
< 0.1%
13001000ix0113630
 
< 0.1%
75114000dm0007595
 
< 0.1%
30189000em0022502
 
< 0.1%
Other values (2142840)3524573
99.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ancien_id_parcelle
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct3366
Distinct (%)81.0%
Missing3529056
Missing (%)99.9%
Memory size27.0 MiB
782510000B0256
 
32
78524000AD0005
 
19
78524000AB0143
 
15
91182000AP0094
 
14
78524000AB0101
 
14
Other values (3361)
4061 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2882 ?
Unique (%)69.4%

Sample

1st row012050000E1483
2nd row012050000E1490
3rd row010910000A1095
4th row010910000A1104
5th row012050000E1401

Common Values

ValueCountFrequency (%)
782510000B025632
 
< 0.1%
78524000AD000519
 
< 0.1%
78524000AB014315
 
< 0.1%
91182000AP009414
 
< 0.1%
78524000AB010114
 
< 0.1%
691470000U036212
 
< 0.1%
91182000AP008712
 
< 0.1%
91182000AP004610
 
< 0.1%
91182000AP00919
 
< 0.1%
91182000AN00288
 
< 0.1%
Other values (3356)4010
 
0.1%
(Missing)3529056
99.9%

Length

2021-10-06T00:25:49.852588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
782510000b025632
 
0.8%
78524000ad000519
 
0.5%
78524000ab014315
 
0.4%
91182000ap009414
 
0.3%
78524000ab010114
 
0.3%
691470000u036212
 
0.3%
91182000ap008712
 
0.3%
91182000ap004610
 
0.2%
91182000ap00919
 
0.2%
91182000ap00598
 
0.2%
Other values (3356)4010
96.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

numero_volume
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3523727
Missing (%)99.7%
Memory size27.0 MiB

lot1_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2436238
Missing (%)69.0%
Memory size27.0 MiB

lot1_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct18559
Distinct (%)6.2%
Missing3234737
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean61.73629629
Minimum0.14
Maximum9646.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:50.120648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.14
5-th percentile16.28
Q133.62
median53.045
Q372.94
95-th percentile116.44
Maximum9646.2
Range9646.06
Interquartile range (IQR)39.32

Descriptive statistics

Standard deviation113.0510994
Coefficient of variation (CV)1.831193417
Kurtosis2712.421666
Mean61.73629629
Median Absolute Deviation (MAD)19.625
Skewness44.33723987
Sum18426679.3
Variance12780.55107
MonotonicityNot monotonic
2021-10-06T00:25:50.426225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.51215
 
< 0.1%
12643
 
< 0.1%
15373
 
< 0.1%
13350
 
< 0.1%
10328
 
< 0.1%
30284
 
< 0.1%
40278
 
< 0.1%
20271
 
< 0.1%
25268
 
< 0.1%
14265
 
< 0.1%
Other values (18549)294199
 
8.3%
(Missing)3234737
91.6%
ValueCountFrequency (%)
0.141
< 0.1%
0.261
< 0.1%
0.361
< 0.1%
0.51
< 0.1%
0.611
< 0.1%
0.641
< 0.1%
0.71
< 0.1%
0.741
< 0.1%
0.81
< 0.1%
0.851
< 0.1%
ValueCountFrequency (%)
9646.21
 
< 0.1%
91011
 
< 0.1%
89461
 
< 0.1%
8875.21
 
< 0.1%
8771.941
 
< 0.1%
8719.91
 
< 0.1%
77171
 
< 0.1%
76341
 
< 0.1%
7597.719
< 0.1%
6422.871
 
< 0.1%

lot2_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3304923
Missing (%)93.5%
Memory size27.0 MiB

lot2_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct12485
Distinct (%)16.8%
Missing3458801
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean63.44957197
Minimum0.13
Maximum8630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:50.719318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile23
Q143
median61.08
Q376.26
95-th percentile110.1
Maximum8630
Range8629.87
Interquartile range (IQR)33.26

Descriptive statistics

Standard deviation53.40071893
Coefficient of variation (CV)0.8416245733
Kurtosis9455.510397
Mean63.44957197
Median Absolute Deviation (MAD)16.78
Skewness67.65985445
Sum4721282.65
Variance2851.636783
MonotonicityNot monotonic
2021-10-06T00:25:50.989407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.16105
 
< 0.1%
74.8399
 
< 0.1%
20.4371
 
< 0.1%
6567
 
< 0.1%
5564
 
< 0.1%
6761
 
< 0.1%
6859
 
< 0.1%
7058
 
< 0.1%
5057
 
< 0.1%
5455
 
< 0.1%
Other values (12475)73714
 
2.1%
(Missing)3458801
97.9%
ValueCountFrequency (%)
0.131
 
< 0.1%
0.31
 
< 0.1%
0.541
 
< 0.1%
0.581
 
< 0.1%
0.631
 
< 0.1%
0.751
 
< 0.1%
0.84
< 0.1%
0.92
< 0.1%
0.911
 
< 0.1%
0.931
 
< 0.1%
ValueCountFrequency (%)
86301
< 0.1%
3583.641
< 0.1%
3006.591
< 0.1%
25201
< 0.1%
2111.681
< 0.1%
1989.71
< 0.1%
1828.921
< 0.1%
1584.421
< 0.1%
1505.321
< 0.1%
1474.61
< 0.1%

lot3_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3495391
Missing (%)98.9%
Memory size27.0 MiB

lot3_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5133
Distinct (%)69.4%
Missing3525820
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean72.66339196
Minimum0.85
Maximum3583.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:51.291491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile14.085
Q138.425
median60.76
Q385.21
95-th percentile167.43
Maximum3583.64
Range3582.79
Interquartile range (IQR)46.785

Descriptive statistics

Standard deviation82.71852482
Coefficient of variation (CV)1.138379624
Kurtosis547.2276462
Mean72.66339196
Median Absolute Deviation (MAD)23.15
Skewness16.83892817
Sum537055.13
Variance6842.354349
MonotonicityNot monotonic
2021-10-06T00:25:51.576572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.3191
 
< 0.1%
20.6169
 
< 0.1%
12.521
 
< 0.1%
195.1821
 
< 0.1%
1515
 
< 0.1%
66.1114
 
< 0.1%
4012
 
< 0.1%
1212
 
< 0.1%
1911
 
< 0.1%
2011
 
< 0.1%
Other values (5123)7114
 
0.2%
(Missing)3525820
99.8%
ValueCountFrequency (%)
0.851
 
< 0.1%
17
< 0.1%
1.131
 
< 0.1%
1.221
 
< 0.1%
1.251
 
< 0.1%
1.31
 
< 0.1%
1.331
 
< 0.1%
1.351
 
< 0.1%
1.531
 
< 0.1%
1.781
 
< 0.1%
ValueCountFrequency (%)
3583.641
< 0.1%
2128.861
< 0.1%
1699.391
< 0.1%
1480.161
< 0.1%
13001
< 0.1%
12401
< 0.1%
1132.31
< 0.1%
10551
< 0.1%
918.11
< 0.1%
8891
< 0.1%

lot4_numero
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct804
Distinct (%)6.1%
Missing3520085
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean141.2614658
Minimum2
Maximum191612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:51.876190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q17
median24
Q371
95-th percentile411
Maximum191612
Range191610
Interquartile range (IQR)64

Descriptive statistics

Standard deviation2245.813621
Coefficient of variation (CV)15.89827494
Kurtosis6072.676851
Mean141.2614658
Median Absolute Deviation (MAD)19
Skewness75.84754127
Sum1854198
Variance5043678.822
MonotonicityNot monotonic
2021-10-06T00:25:52.193262image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9783
 
< 0.1%
8733
 
< 0.1%
7722
 
< 0.1%
4681
 
< 0.1%
6678
 
< 0.1%
5631
 
< 0.1%
3323
 
< 0.1%
2249
 
< 0.1%
13223
 
< 0.1%
17150
 
< 0.1%
Other values (794)7953
 
0.2%
(Missing)3520085
99.6%
ValueCountFrequency (%)
2249
 
< 0.1%
3323
< 0.1%
4681
< 0.1%
5631
< 0.1%
6678
< 0.1%
7722
< 0.1%
8733
< 0.1%
9783
< 0.1%
118
 
< 0.1%
12118
 
< 0.1%
ValueCountFrequency (%)
1916121
< 0.1%
1618161
< 0.1%
200351
< 0.1%
170141
< 0.1%
112791
< 0.1%
110451
< 0.1%
110431
< 0.1%
110411
< 0.1%
107291
< 0.1%
105201
< 0.1%

lot4_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1558
Distinct (%)76.7%
Missing3531180
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean88.28162974
Minimum1
Maximum5107.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:52.534339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.52
Q133.285
median65.09
Q397.635
95-th percentile210.42
Maximum5107.9
Range5106.9
Interquartile range (IQR)64.35

Descriptive statistics

Standard deviation175.4470496
Coefficient of variation (CV)1.987356261
Kurtosis398.0506412
Mean88.28162974
Median Absolute Deviation (MAD)32.07
Skewness16.78892705
Sum179299.99
Variance30781.66721
MonotonicityNot monotonic
2021-10-06T00:25:53.009446image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.8790
 
< 0.1%
20.9968
 
< 0.1%
115.7521
 
< 0.1%
51.4414
 
< 0.1%
63.8813
 
< 0.1%
12.510
 
< 0.1%
208
 
< 0.1%
53.938
 
< 0.1%
57.927
 
< 0.1%
14.846
 
< 0.1%
Other values (1548)1786
 
0.1%
(Missing)3531180
99.9%
ValueCountFrequency (%)
13
< 0.1%
1.551
 
< 0.1%
1.61
 
< 0.1%
1.81
 
< 0.1%
21
 
< 0.1%
2.331
 
< 0.1%
2.871
 
< 0.1%
3.091
 
< 0.1%
3.41
 
< 0.1%
3.651
 
< 0.1%
ValueCountFrequency (%)
5107.91
< 0.1%
3208.91
< 0.1%
20231
< 0.1%
16121
< 0.1%
1588.651
< 0.1%
1588.251
< 0.1%
1415.961
< 0.1%
1362.61
< 0.1%
13001
< 0.1%
10551
< 0.1%

lot5_numero
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct585
Distinct (%)9.2%
Missing3526863
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean183.1717076
Minimum2
Maximum191613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:53.315514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median28
Q382
95-th percentile576.65
Maximum191613
Range191611
Interquartile range (IQR)74

Descriptive statistics

Standard deviation3186.490293
Coefficient of variation (CV)17.39619254
Kurtosis3098.016362
Mean183.1717076
Median Absolute Deviation (MAD)22
Skewness54.84380155
Sum1162774
Variance10153720.39
MonotonicityNot monotonic
2021-10-06T00:25:53.608584image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9366
 
< 0.1%
8360
 
< 0.1%
5339
 
< 0.1%
6305
 
< 0.1%
7302
 
< 0.1%
4191
 
< 0.1%
3154
 
< 0.1%
14132
 
< 0.1%
70591
 
< 0.1%
282
 
< 0.1%
Other values (575)4026
 
0.1%
(Missing)3526863
99.8%
ValueCountFrequency (%)
282
 
< 0.1%
3154
< 0.1%
4191
< 0.1%
5339
< 0.1%
6305
< 0.1%
7302
< 0.1%
8360
< 0.1%
9366
< 0.1%
111
 
< 0.1%
123
 
< 0.1%
ValueCountFrequency (%)
1916131
< 0.1%
1619151
< 0.1%
200361
< 0.1%
110461
< 0.1%
70621
< 0.1%
70362
< 0.1%
70052
< 0.1%
61751
< 0.1%
60741
< 0.1%
60341
< 0.1%

lot5_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct676
Distinct (%)71.5%
Missing3532265
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean91.14411205
Minimum0.89
Maximum1029.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:53.949187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.89
5-th percentile11.4875
Q126.925
median74.1
Q3107.135
95-th percentile278.3975
Maximum1029.55
Range1028.66
Interquartile range (IQR)80.21

Descriptive statistics

Standard deviation102.5548555
Coefficient of variation (CV)1.125194521
Kurtosis17.67653912
Mean91.14411205
Median Absolute Deviation (MAD)41.92
Skewness3.473662083
Sum86222.33
Variance10517.49838
MonotonicityNot monotonic
2021-10-06T00:25:54.244258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7790
 
< 0.1%
23.7968
 
< 0.1%
116.121
 
< 0.1%
32.6713
 
< 0.1%
34.847
 
< 0.1%
12.57
 
< 0.1%
55.157
 
< 0.1%
21.446
 
< 0.1%
125
 
< 0.1%
21.14
 
< 0.1%
Other values (666)718
 
< 0.1%
(Missing)3532265
> 99.9%
ValueCountFrequency (%)
0.891
 
< 0.1%
0.971
 
< 0.1%
1.31
 
< 0.1%
21
 
< 0.1%
2.081
 
< 0.1%
2.231
 
< 0.1%
2.961
 
< 0.1%
3.21
 
< 0.1%
3.83
< 0.1%
3.911
 
< 0.1%
ValueCountFrequency (%)
1029.551
< 0.1%
818.81
< 0.1%
7691
< 0.1%
671.91
< 0.1%
662.351
< 0.1%
635.691
< 0.1%
613.71
< 0.1%
603.041
< 0.1%
567.431
< 0.1%
509.151
< 0.1%

nombre_lots
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3993333543
Minimum0
Maximum175
Zeros2436238
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:54.572625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum175
Range175
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9237460492
Coefficient of variation (CV)2.313220369
Kurtosis3942.605237
Mean0.3993333543
Median Absolute Deviation (MAD)0
Skewness39.52830246
Sum1410929
Variance0.8533067635
MonotonicityNot monotonic
2021-10-06T00:25:54.893861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02436238
69.0%
1868685
 
24.6%
2190468
 
5.4%
324694
 
0.7%
46778
 
0.2%
52526
 
0.1%
61280
 
< 0.1%
7679
 
< 0.1%
8433
 
< 0.1%
9278
 
< 0.1%
Other values (76)1152
 
< 0.1%
ValueCountFrequency (%)
02436238
69.0%
1868685
 
24.6%
2190468
 
5.4%
324694
 
0.7%
46778
 
0.2%
52526
 
0.1%
61280
 
< 0.1%
7679
 
< 0.1%
8433
 
< 0.1%
9278
 
< 0.1%
ValueCountFrequency (%)
1751
< 0.1%
1591
< 0.1%
1501
< 0.1%
1421
< 0.1%
1411
< 0.1%
1361
< 0.1%
1181
< 0.1%
1141
< 0.1%
1101
< 0.1%
1042
< 0.1%

code_type_local
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1632190
Missing (%)46.2%
Memory size27.0 MiB
1.0
698631 
2.0
614886 
3.0
450801 
4.0
136703 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0698631
19.8%
2.0614886
 
17.4%
3.0450801
 
12.8%
4.0136703
 
3.9%
(Missing)1632190
46.2%

Length

2021-10-06T00:25:55.212295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-06T00:25:55.382334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0698631
36.8%
2.0614886
32.3%
3.0450801
23.7%
4.0136703
 
7.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

type_local
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1632190
Missing (%)46.2%
Memory size27.0 MiB
Maison
698631 
Appartement
614886 
Dépendance
450801 
Local industriel. commercial ou assimilé
136703 

Length

Max length40
Median length10
Mean length11.01074738
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAppartement
2nd rowDépendance
3rd rowAppartement
4th rowMaison
5th rowMaison

Common Values

ValueCountFrequency (%)
Maison698631
19.8%
Appartement614886
 
17.4%
Dépendance450801
 
12.8%
Local industriel. commercial ou assimilé136703
 
3.9%
(Missing)1632190
46.2%

Length

2021-10-06T00:25:55.631900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-06T00:25:55.831945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
maison698631
28.5%
appartement614886
25.1%
dépendance450801
18.4%
assimilé136703
 
5.6%
ou136703
 
5.6%
commercial136703
 
5.6%
industriel136703
 
5.6%
local136703
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

surface_reelle_bati
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct4598
Distinct (%)0.3%
Missing2090899
Missing (%)59.2%
Infinite0
Infinite (%)0.0%
Mean114.5582745
Minimum1
Maximum312962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:56.108010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q149
median75
Q3104
95-th percentile190
Maximum312962
Range312961
Interquartile range (IQR)55

Descriptive statistics

Standard deviation909.0004014
Coefficient of variation (CV)7.934829723
Kurtosis40237.20298
Mean114.5582745
Median Absolute Deviation (MAD)27
Skewness162.9347308
Sum165228774
Variance826281.7298
MonotonicityNot monotonic
2021-10-06T00:25:56.401586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8028149
 
0.8%
6026035
 
0.7%
9025149
 
0.7%
7025027
 
0.7%
10021679
 
0.6%
5021599
 
0.6%
6520212
 
0.6%
4019079
 
0.5%
7517538
 
0.5%
3016977
 
0.5%
Other values (4588)1220868
34.6%
(Missing)2090899
59.2%
ValueCountFrequency (%)
1345
 
< 0.1%
2448
 
< 0.1%
3356
 
< 0.1%
4229
 
< 0.1%
5327
 
< 0.1%
6436
 
< 0.1%
7356
 
< 0.1%
81074
 
< 0.1%
91140
 
< 0.1%
103914
0.1%
ValueCountFrequency (%)
3129622
 
< 0.1%
2400002
 
< 0.1%
2150002
 
< 0.1%
2121202
 
< 0.1%
1528566
< 0.1%
1425361
 
< 0.1%
1421241
 
< 0.1%
1127551
 
< 0.1%
1074761
 
< 0.1%
1047601
 
< 0.1%

nombre_pieces_principales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct40
Distinct (%)< 0.1%
Missing1634823
Missing (%)46.3%
Infinite0
Infinite (%)0.0%
Mean2.3919852
Minimum0
Maximum67
Zeros587272
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:56.716658image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile6
Maximum67
Range67
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.079934991
Coefficient of variation (CV)0.86954342
Kurtosis2.596047971
Mean2.3919852
Median Absolute Deviation (MAD)2
Skewness0.5953746862
Sum4540916
Variance4.326129569
MonotonicityNot monotonic
2021-10-06T00:25:57.013246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0587272
 
16.6%
4320521
 
9.1%
3314175
 
8.9%
2230368
 
6.5%
5188126
 
5.3%
1141803
 
4.0%
672887
 
2.1%
726420
 
0.7%
89738
 
0.3%
93609
 
0.1%
Other values (30)3469
 
0.1%
(Missing)1634823
46.3%
ValueCountFrequency (%)
0587272
16.6%
1141803
 
4.0%
2230368
 
6.5%
3314175
8.9%
4320521
9.1%
5188126
 
5.3%
672887
 
2.1%
726420
 
0.7%
89738
 
0.3%
93609
 
0.1%
ValueCountFrequency (%)
671
 
< 0.1%
562
< 0.1%
541
 
< 0.1%
533
< 0.1%
502
< 0.1%
411
 
< 0.1%
352
< 0.1%
341
 
< 0.1%
332
< 0.1%
322
< 0.1%

code_nature_culture
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)< 0.1%
Missing1112059
Missing (%)31.5%
Memory size27.0 MiB
S
1151464 
T
359174 
P
190040 
AB
127433 
J
123089 
Other values (22)
469952 

Length

Max length2
Median length1
Mean length1.197536958
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowL

Common Values

ValueCountFrequency (%)
S1151464
32.6%
T359174
 
10.2%
P190040
 
5.4%
AB127433
 
3.6%
J123089
 
3.5%
BT103871
 
2.9%
L96258
 
2.7%
AG84735
 
2.4%
VI41396
 
1.2%
BR34436
 
1.0%
Other values (17)109256
 
3.1%
(Missing)1112059
31.5%

Length

2021-10-06T00:25:57.376327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s1151464
47.6%
t359174
 
14.8%
p190040
 
7.8%
ab127433
 
5.3%
j123089
 
5.1%
bt103871
 
4.3%
l96258
 
4.0%
ag84735
 
3.5%
vi41396
 
1.7%
br34436
 
1.4%
Other values (17)109256
 
4.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nature_culture
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)< 0.1%
Missing1112059
Missing (%)31.5%
Memory size27.0 MiB
sols
1151464 
terres
359174 
prés
190040 
terrains a bâtir
127433 
jardins
123089 
Other values (22)
469952 

Length

Max length19
Median length4
Mean length6.638552639
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsols
2nd rowsols
3rd rowsols
4th rowsols
5th rowlandes

Common Values

ValueCountFrequency (%)
sols1151464
32.6%
terres359174
 
10.2%
prés190040
 
5.4%
terrains a bâtir127433
 
3.6%
jardins123089
 
3.5%
taillis simples103871
 
2.9%
landes96258
 
2.7%
terrains d'agrément84735
 
2.4%
vignes41396
 
1.2%
futaies résineuses34436
 
1.0%
Other values (17)109256
 
3.1%
(Missing)1112059
31.5%

Length

2021-10-06T00:25:57.838436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sols1151464
39.1%
terres359282
 
12.2%
terrains212168
 
7.2%
prés192687
 
6.5%
a127433
 
4.3%
bâtir127433
 
4.3%
jardins123089
 
4.2%
taillis121598
 
4.1%
simples103871
 
3.5%
landes96592
 
3.3%
Other values (24)331523
 
11.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_nature_culture_speciale
Categorical

HIGH CARDINALITY
MISSING

Distinct120
Distinct (%)0.1%
Missing3375916
Missing (%)95.5%
Memory size27.0 MiB
POTAG
34964 
PATUR
15855 
PARC
13383 
PIN
12642 
FRICH
10625 
Other values (115)
69826 

Length

Max length5
Median length5
Mean length4.498534601
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowACACI
2nd rowFRICH
3rd rowPATUR
4th rowFRICH
5th rowFRICH

Common Values

ValueCountFrequency (%)
POTAG34964
 
1.0%
PATUR15855
 
0.4%
PARC13383
 
0.4%
PIN12642
 
0.4%
FRICH10625
 
0.3%
VAOC7371
 
0.2%
CHAT5181
 
0.1%
IMM4363
 
0.1%
CHENE3885
 
0.1%
MARAI3456
 
0.1%
Other values (110)45570
 
1.3%
(Missing)3375916
95.5%

Length

2021-10-06T00:25:58.126501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
potag34964
22.2%
patur15855
 
10.1%
parc13383
 
8.5%
pin12642
 
8.0%
frich10625
 
6.8%
vaoc7371
 
4.7%
chat5181
 
3.3%
imm4363
 
2.8%
chene3885
 
2.5%
marai3456
 
2.2%
Other values (110)45570
29.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nature_culture_speciale
Categorical

HIGH CARDINALITY
MISSING

Distinct120
Distinct (%)0.1%
Missing3375916
Missing (%)95.5%
Memory size27.0 MiB
Jardin potager
34964 
Pâture plantée
15855 
Parc
13383 
Pins
12642 
Friche
10625 
Other values (115)
69826 

Length

Max length38
Median length14
Mean length12.5603611
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowAcacias
2nd rowFriche
3rd rowPâture plantée
4th rowFriche
5th rowFriche

Common Values

ValueCountFrequency (%)
Jardin potager34964
 
1.0%
Pâture plantée15855
 
0.4%
Parc13383
 
0.4%
Pins12642
 
0.4%
Friche10625
 
0.3%
Vins d'appellation d'origine contrôlée7371
 
0.2%
Châtaigneraie5181
 
0.1%
Dépendances d'ensemble immobilier4363
 
0.1%
Chênes3885
 
0.1%
Pré marais3456
 
0.1%
Other values (110)45570
 
1.3%
(Missing)3375916
95.5%

Length

2021-10-06T00:25:58.419570image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jardin36478
 
12.8%
potager34964
 
12.2%
pâture15855
 
5.5%
plantée15855
 
5.5%
parc13387
 
4.7%
pins12642
 
4.4%
friche10625
 
3.7%
ou8329
 
2.9%
vins7549
 
2.6%
contrôlée7371
 
2.6%
Other values (154)122781
43.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

surface_terrain
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct47002
Distinct (%)1.9%
Missing1112128
Missing (%)31.5%
Infinite0
Infinite (%)0.0%
Mean3074.833515
Minimum1
Maximum2047961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 MiB
2021-10-06T00:25:58.725639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q1235
median623
Q31930
95-th percentile12700
Maximum2047961
Range2047960
Interquartile range (IQR)1695

Descriptive statistics

Standard deviation13291.92486
Coefficient of variation (CV)4.322811235
Kurtosis4022.718132
Mean3074.833515
Median Absolute Deviation (MAD)500
Skewness45.59182149
Sum7444427151
Variance176675266.6
MonotonicityNot monotonic
2021-10-06T00:25:59.005724image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50046822
 
1.3%
100022377
 
0.6%
6006915
 
0.2%
8006849
 
0.2%
126148
 
0.2%
7005766
 
0.2%
4005718
 
0.2%
135609
 
0.2%
1005409
 
0.2%
2005371
 
0.2%
Other values (46992)2304099
65.2%
(Missing)1112128
31.5%
ValueCountFrequency (%)
15124
0.1%
24143
0.1%
33848
0.1%
43956
0.1%
54051
0.1%
63966
0.1%
73688
0.1%
83785
0.1%
93687
0.1%
104781
0.1%
ValueCountFrequency (%)
20479611
 
< 0.1%
18892071
 
< 0.1%
16625601
 
< 0.1%
14591431
 
< 0.1%
14203881
 
< 0.1%
141152414
< 0.1%
133500319
< 0.1%
132217733
< 0.1%
12502231
 
< 0.1%
12043951
 
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1888639
Distinct (%)54.2%
Missing51669
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean2.212156196
Minimum-63.147439
Maximum55.83136
Zeros1
Zeros (%)< 0.1%
Negative811107
Negative (%)23.0%
Memory size27.0 MiB
2021-10-06T00:25:59.295303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-63.147439
5-th percentile-2.245149
Q10.177443
median2.321533
Q34.395876
95-th percentile6.605287
Maximum55.83136
Range118.978799
Interquartile range (IQR)4.218433

Descriptive statistics

Standard deviation6.292895562
Coefficient of variation (CV)2.844688623
Kurtosis67.79648593
Mean2.212156196
Median Absolute Deviation (MAD)2.117716
Skewness-1.590094114
Sum7701714.707
Variance39.60053455
MonotonicityNot monotonic
2021-10-06T00:25:59.626378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.4593321694
 
< 0.1%
2.2889961044
 
< 0.1%
2.1692711013
 
< 0.1%
2.395374971
 
< 0.1%
2.46163806
 
< 0.1%
2.042915701
 
< 0.1%
2.324086687
 
< 0.1%
2.311923636
 
< 0.1%
5.393182631
 
< 0.1%
4.33429502
 
< 0.1%
Other values (1888629)3472857
98.3%
(Missing)51669
 
1.5%
ValueCountFrequency (%)
-63.1474391
 
< 0.1%
-63.1429034
< 0.1%
-63.142644
< 0.1%
-63.1381811
 
< 0.1%
-63.1361598
< 0.1%
-63.1330471
 
< 0.1%
-63.1330451
 
< 0.1%
-63.132626
< 0.1%
-63.1324663
 
< 0.1%
-63.1150443
 
< 0.1%
ValueCountFrequency (%)
55.831361
< 0.1%
55.8305331
< 0.1%
55.8304461
< 0.1%
55.8299852
< 0.1%
55.8290311
< 0.1%
55.8286071
< 0.1%
55.8283211
< 0.1%
55.8279191
< 0.1%
55.8277271
< 0.1%
55.8275481
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1820702
Distinct (%)52.3%
Missing51669
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean46.20441239
Minimum-21.385063
Maximum51.082118
Zeros0
Zeros (%)0.0%
Negative17196
Negative (%)0.5%
Memory size27.0 MiB
2021-10-06T00:26:00.010466image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-21.385063
5-th percentile43.25579005
Q144.808463
median46.897582
Q348.70829
95-th percentile49.8812955
Maximum51.082118
Range72.467181
Interquartile range (IQR)3.899827

Descriptive statistics

Standard deviation5.705283676
Coefficient of variation (CV)0.1234791956
Kurtosis98.34584576
Mean46.20441239
Median Absolute Deviation (MAD)1.901368
Skewness-9.016514683
Sum160862602.3
Variance32.55026183
MonotonicityNot monotonic
2021-10-06T00:26:00.280868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.0372331693
 
< 0.1%
48.6882021044
 
< 0.1%
48.7577891013
 
< 0.1%
48.713603971
 
< 0.1%
48.90182806
 
< 0.1%
48.811098698
 
< 0.1%
48.957154691
 
< 0.1%
43.498437631
 
< 0.1%
48.831863596
 
< 0.1%
43.823295502
 
< 0.1%
Other values (1820692)3472897
98.3%
(Missing)51669
 
1.5%
ValueCountFrequency (%)
-21.3850631
< 0.1%
-21.3849261
< 0.1%
-21.3847661
< 0.1%
-21.3846691
< 0.1%
-21.3846031
< 0.1%
-21.3845991
< 0.1%
-21.384581
< 0.1%
-21.384531
< 0.1%
-21.3844971
< 0.1%
-21.3844291
< 0.1%
ValueCountFrequency (%)
51.0821186
< 0.1%
51.0820454
 
< 0.1%
51.0819474
 
< 0.1%
51.0818054
 
< 0.1%
51.0817652
 
< 0.1%
51.0816314
 
< 0.1%
51.0815763
 
< 0.1%
51.0813756
< 0.1%
51.08110213
< 0.1%
51.0807655
 
< 0.1%

Interactions

2021-10-06T00:23:22.141127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:18:42.797327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:19:08.406285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:19:35.525324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:19:57.753198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:24.094399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:31.869924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:40.857022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:48.275429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:55.547706image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:03.078525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:10.400391image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:17.959726image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:27.391494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:52.489360image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:10.613375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:31.797937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:53.619572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:23:25.002231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:18:45.454988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:19:11.260092image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:19:37.406703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:00.699461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:24.480101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:32.527356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:41.316126image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:48.703036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:55.982380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:03.531139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:10.903505image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:18.428851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:30.013634image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:54.024741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:12.406449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:33.899438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:56.564241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:23:26.968252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:18:47.107380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:19:13.212076image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:19:39.369514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:02.639482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:24.851186image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:33.102525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:41.734236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:49.150110image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:56.605133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:03.914113image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:11.315106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:18.852368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:31.926801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:55.450846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:14.256113image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:35.100766image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:58.504493image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:23:29.868152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:18:49.584336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:19:16.007694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:19:41.202513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:05.512372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:25.206856image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:33.744981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:42.203342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:49.527704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:57.019746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:04.283206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:11.720202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:19.267524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:34.514967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:56.864541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:16.005662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:37.248853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:23:01.562440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:23:30.274285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:18:50.060463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:19:16.397824image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:19:41.639119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:05.921471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T00:21:09.278567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:16.789031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:24.069329image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:45.699257image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:05.868159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:25.961232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:46.240270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:23:13.357821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:23:45.372463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:19:03.011338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T00:19:54.748127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:23.669303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:31.190278image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T00:20:47.822301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:20:55.119086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:02.666432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:10.006793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:17.527224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:24.809605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:21:50.997473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:22:08.790428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T00:22:50.480336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:23:19.234217image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-10-06T00:26:00.593940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-06T00:26:01.333667image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-06T00:26:02.114351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-06T00:26:02.831935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-06T00:26:03.254031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-06T00:23:56.644026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-06T00:24:15.762082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-06T00:25:10.380264image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-06T00:25:24.319319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

id_mutationdate_mutationnumero_dispositionnature_mutationvaleur_fonciereadresse_numeroadresse_suffixeadresse_nom_voieadresse_code_voiecode_postalcode_communenom_communecode_departementancien_code_communeancien_nom_communeid_parcelleancien_id_parcellenumero_volumelot1_numerolot1_surface_carrezlot2_numerolot2_surface_carrezlot3_numerolot3_surface_carrezlot4_numerolot4_surface_carrezlot5_numerolot5_surface_carreznombre_lotscode_type_localtype_localsurface_reelle_batinombre_pieces_principalescode_nature_culturenature_culturecode_nature_culture_specialenature_culture_specialesurface_terrainlongitudelatitude
02019-12019-01-041Vente37220.026.0NaNRUE DE MONTHOLON27301000.01053Bourg-en-Bresse1NaNNaN01053000AI0298NaNNaN819.27NaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement20.01.0NaNNaNNaNNaNNaN5.20957846.198844
12019-22019-01-041Vente185100.022.0BRUE GEN DELESTRAINT16501000.01053Bourg-en-Bresse1NaNNaN01053000AM0095NaNNaN7NaNNaNNaNNaNNaNNaNNaNNaNNaN13.0DépendanceNaN0.0NaNNaNNaNNaNNaN5.21943846.198784
22019-22019-01-041Vente185100.022.0NaNRUE GEN DELESTRAINT16501000.01053Bourg-en-Bresse1NaNNaN01053000AM0095NaNNaN137NaN15461.51NaNNaNNaNNaNNaNNaN22.0Appartement62.03.0NaNNaNNaNNaNNaN5.21943846.198784
32019-32019-01-081Vente209000.03.0NaNRUE DES CHAMPAGNES00431160.01314Priay1NaNNaN013140000E1676NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN01.0Maison90.04.0SsolsNaNNaN940.05.28208545.999533
42019-42019-01-071Vente134900.05.0NaNLOT LE BIOLAYA0031370.01350Saint-Étienne-du-Bois1NaNNaN01350000AA0011NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN01.0Maison101.05.0SsolsNaNNaN490.05.29991946.293275
52019-52019-01-031Vente192000.0165.0NaNALL DES LIBELLULES04451340.01024Attignat1NaNNaN01024000AI0094NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN01.0Maison88.04.0SsolsNaNNaN708.05.17478746.269969
62019-62019-01-081Vente45000.09.0NaNRTE DU VIADUC00011250.01106Cize1NaNNaN011060000A0086NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN01.0Maison39.02.0SsolsNaNNaN631.05.45213446.205259
72019-62019-01-081Vente45000.0NaNNaNSUR LA LATIEB0171250.01106Cize1NaNNaN011060000A0975NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNLlandesNaNNaN120.05.45214146.205327
82019-72019-01-041Vente65000.050.0NaNRUE DOC NODET12401000.01053Bourg-en-Bresse1NaNNaN01053000AL0003NaNNaN317NaNNaNNaNNaNNaNNaNNaNNaNNaN13.0DépendanceNaN0.0NaNNaNNaNNaNNaN5.22065246.193394
92019-72019-01-041Vente65000.050.0NaNRUE DOC NODET12401000.01053Bourg-en-Bresse1NaNNaN01053000AL0003NaNNaN17NaN3367.78NaNNaNNaNNaNNaNNaN22.0Appartement69.03.0NaNNaNNaNNaNNaN5.22065246.193394

Last rows

id_mutationdate_mutationnumero_dispositionnature_mutationvaleur_fonciereadresse_numeroadresse_suffixeadresse_nom_voieadresse_code_voiecode_postalcode_communenom_communecode_departementancien_code_communeancien_nom_communeid_parcelleancien_id_parcellenumero_volumelot1_numerolot1_surface_carrezlot2_numerolot2_surface_carrezlot3_numerolot3_surface_carrezlot4_numerolot4_surface_carrezlot5_numerolot5_surface_carreznombre_lotscode_type_localtype_localsurface_reelle_batinombre_pieces_principalescode_nature_culturenature_culturecode_nature_culture_specialenature_culture_specialesurface_terrainlongitudelatitude
35332012019-15148662019-12-051Vente17521000.032.0NaNQUAI DE BETHUNE094075004.075104Paris 4e Arrondissement75NaNNaN75104000AU0011NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN03.0DépendanceNaN0.0SsolsNaNNaN470.02.35673048.851148
35332022019-15148662019-12-051Vente17521000.032.0NaNQUAI DE BETHUNE094075004.075104Paris 4e Arrondissement75NaNNaN75104000AU0011NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN02.0Appartement120.05.0SsolsNaNNaN470.02.35673048.851148
35332032019-15148662019-12-051Vente17521000.032.0NaNQUAI DE BETHUNE094075004.075104Paris 4e Arrondissement75NaNNaN75104000AU0011NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN02.0Appartement61.04.0SsolsNaNNaN470.02.35673048.851148
35332042019-15148672019-10-101Adjudication610000.012.0NaNRUE BEAUTREILLIS079775004.075104Paris 4e Arrondissement75NaNNaN75104000AQ0040NaNNaN2NaN35NaNNaNNaNNaNNaNNaNNaN22.0Appartement44.02.0NaNNaNNaNNaNNaN2.36388648.852872
35332052019-15148682019-12-301Vente1400000.024.0NaNRUE SAINT SAUVEUR875275002.075102Paris 2e Arrondissement75NaNNaN75102000AM0018NaNNaN3101.40443.78NaNNaNNaNNaNNaN34.0Local industriel. commercial ou assimilé100.00.0NaNNaNNaNNaNNaN2.34808248.866421
35332062019-15148682019-12-301Vente1400000.024.0NaNRUE SAINT SAUVEUR875275002.075102Paris 2e Arrondissement75NaNNaN75102000AM0018NaNNaN1043.701455.4NaNNaNNaNNaNNaNNaN22.0Appartement97.03.0NaNNaNNaNNaNNaN2.34808248.866421
35332072019-15148692019-12-171Adjudication620000.014.0NaNRUE PAVEE719575004.075104Paris 4e Arrondissement75NaNNaN75104000AM0014NaNNaN348.504NaNNaNNaNNaNNaNNaNNaN22.0Appartement45.02.0NaNNaNNaNNaNNaN2.36071448.856181
35332082019-15148702019-12-051Vente370000.023.0NaNRUE POISSONNIERE756175002.075102Paris 2e Arrondissement75NaNNaN75102000AH0067NaNNaN138.658NaNNaNNaNNaNNaNNaNNaN24.0Local industriel. commercial ou assimilé47.00.0NaNNaNNaNNaNNaN2.34749748.869727
35332092019-15148712019-12-121Adjudication44000.02.0NaNRUE NOTRE DAME DES VICTOIRES684075002.075102Paris 2e Arrondissement75NaNNaN75102000AJ0127NaNNaN41NaNNaNNaNNaNNaNNaNNaNNaNNaN13.0DépendanceNaN0.0NaNNaNNaNNaNNaN2.34124648.866478
35332102019-15148722019-12-261Vente25000.017.0NaNRUE BLONDEL102175002.075102Paris 2e Arrondissement75NaNNaN75102000AP0053NaNNaN34NaNNaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement4.01.0NaNNaNNaNNaNNaN2.35308248.868707